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Neighborhoods to Nucleotides—Advances and Gaps for an Obesity Disparities Systems Epidemiology Model

  • Marta M. JankowskaEmail author
  • Kyle Gaulton
  • Rob Knight
  • Kevin Patrick
  • Dorothy D. Sears
Genetic Epidemiology (C Amos, Section Editor)
  • 17 Downloads
Part of the following topical collections:
  1. Topical Collection on Genetic Epidemiology

Abstract

Purpose of Review

Disparities in prevalence of obesity in the USA continue to increase. Here, we review progress and highlight gaps in understanding disparities in obesity with a focus on the Hispanic/Latino population from a systems epidemiology framework. We review seven domains: environment, behavior, biomarkers, nutrition, microbiome, genomics, and epigenomics/transcriptomics. We focus on recent advances that integrate at least two or more of these domains, and then provide a real-world example of data collection efforts that encompass these domains.

Recent Findings

Research into discrimination-related DNA methylation patterns and how microbiome profiles are related to eating and physical activity behaviors is furthering understanding of why disparities in obesity persist. Environmental and neighborhood level research is uncovering the importance of exposures such as air and noise pollution and systematic or structural racism for obesity and related outcomes through behaviors such as sleep.

Summary

Obesity disparities and the biological processes associated with them must be better contextualized within the social, economic, and political environments that contribute to them. One avenue for accomplishing this is by modeling relationships between within-body mechanisms and omics and beyond-body mechanisms and exposures. However, data integration across the various domains and data collection are significant challenges for generating a comprehensive systems model for obesity disparities.

Keywords

Health disparities Hispanic/Latino Obesity Systems epidemiology Environmental exposure Data integration 

Notes

Funding Information

Funding for this research was provided by a grant from the National Institutes of Health, National Cancer Institute (R01 CA179977). The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. The Nucleotides to Neighborhoods study was a Demonstration Project in Systems Biomedicine supported by a grant from the University of California San Diego Center for Computational Biology and Bioinformatics and San Diego Center for Systems Biology.

Compliance with Ethical Standards

Conflict of Interest

Marta M. Jankowska, Kyle Gaulton, Rob Knight, Kevin Patrick, and Dorothy D. Sears each declare no potential conflicts of interest.

Human and Animal Rights and Informed Consent

This article does not contain any studies with human or animal subjects performed by any of the authors.

References

Papers of particular interest, published recently, have been highlighted as: • Of importance •• Of major importance

  1. 1.
    WHO (World Health Organization) (2013) WHO obesity and overweight fact sheet no 311. Obes Oveweight Fact Sheet.Google Scholar
  2. 2.
    Daviglus ML, Talavera GA, Avilés-Santa ML, et al. Prevalence of major cardiovascular risk factors and cardiovascular diseases among Hispanic/Latino individuals of diverse backgrounds in the United States. JAMA. 2012;308:1775.  https://doi.org/10.1001/jama.2012.14517.CrossRefPubMedPubMedCentralGoogle Scholar
  3. 3.
    Hales CM, Carroll MD, Fryar CD, Ogden CL (2017) prevalence of obesity among adults and youth: United States, 2015–2016.Google Scholar
  4. 4.
    Schneiderman N, Llabre M, Cowie CC, et al. Prevalence of diabetes among Hispanics/Latinos from diverse backgrounds: the Hispanic community health study/study of Latinos (HCHS/SOL). Diabetes Care. 2014;37:2233–9.  https://doi.org/10.2337/dc13-2939.CrossRefPubMedPubMedCentralGoogle Scholar
  5. 5.
    U.S. Centers for Disease Control and Prevention (2011) National diabetes fact sheet: national estimates and general information on diabetes and prediabetes in the United States, 2011. US Dep Heal Hum Serv Centers Dis Control Prev 3:1–12. https://doi.org/201
  6. 6.
    Butland B, Jebb S. Kopelman P, et al. Foresight Tackling Obesities: Future Choices. Project Report. London; 2007.Google Scholar
  7. 7.
    Glass TA, McAtee MJ. Behavioral science at the crossroads in public health: extending horizons, envisioning the future. Soc Sci Med. 2006.  https://doi.org/10.1016/j.socscimed.2005.08.044.
  8. 8.
    Adela Hruby, PhD M, Frank B. Hu, MD, PhD M (2015) The epidemiology of obesity: a big picture. Pharmacoeconomics 33:673–689.  https://doi.org/10.1007/s40273-014-0243-x.
  9. 9.
    Mabry PL, Kaplan RM. Systems science: a good investment for the public’s health. Health Educ Behav. 2013;40:9S–12S.  https://doi.org/10.1177/1090198113503469.CrossRefPubMedPubMedCentralGoogle Scholar
  10. 10.
    Fowler SP, Puppala S, Arya R, et al. Genetic epidemiology of cardiometabolic risk factors and their clustering patterns in Mexican American children and adolescents: the SAFARI study. Hum Genet. 2013;132:1059–71.  https://doi.org/10.1007/s00439-013-1315-2.CrossRefPubMedPubMedCentralGoogle Scholar
  11. 11.
    Comuzzie AG, Cole SA, Laston SL, et al. Novel genetic loci identified for the pathophysiology of childhood obesity in the Hispanic population. PLoS One. 2012.  https://doi.org/10.1371/journal.pone.0051954.
  12. 12.
    Mathers JC. Nutrigenomics in the modern era. In: Proceedings of the Nutrition Society; 2017.CrossRefGoogle Scholar
  13. 13.
    •• Klimentidis YC, Raichlen DA, Bea J, et al. Genome-wide association study of habitual physical activity in over 377,000 UK biobank participants identifies multiple variants including CADM2 and APOE. Int J Obes. 2018;42:1161–76.  https://doi.org/10.1038/s41366-018-0120-3. First GWAS study to examine genetic heritablility of habitual exercise (measured with both self report and actigraphy). CrossRefGoogle Scholar
  14. 14.
    • Robinette JW, Boardman JD, Crimmins EM (2019) Differential vulnerability to neighbourhood disorder: A gene×environment interaction study. J Epidemiol Community Health 73:.  https://doi.org/10.1136/jech-2018-211373. Examines effects of genetic markers of type 2 diabetes and self-reported perceptions of environmental disorder on type 2 diabetes outcomes finding positive associations.
  15. 15.
    • Le Roy CI, Beaumont M, Jackson MA, et al. Heritable components of the human fecal microbiome are associated with visceral fat. Gut Microbes. 2018;9:61–7.  https://doi.org/10.1080/19490976.2017.1356556. Builds on previous research in the TwinsUK cohort demonstrating that heritable micorbial OTUs are associated with accumulation of visceral fat phenotype. CrossRefPubMedPubMedCentralGoogle Scholar
  16. 16.
    Davis JN, Lê KA, Walker RW, et al. Increased hepatic fat in overweight Hispanic youth influenced by interaction between genetic variation in PNPLA3 and high dietary carbohydrate and sugar consumption. Am J Clin Nutr. 2010.  https://doi.org/10.3945/ajcn.2010.30185.
  17. 17.
    •• Moon JY, Wang T, Sofer T, et al. Objectively measured physical activity, sedentary behavior, and genetic predisposition to obesity in U.S. Hispanics/Latinos: results from the hispanic community health study/study of Latinos (HCHS/SOL). Diabetes. 2017.  https://doi.org/10.2337/db17-0573. First study to examine interactions between accelerometer measured physical activity/sednetary behavior and genetic variants on obesity in a large Hispanic/Latino cohort.
  18. 18.
    Conomos MP, Laurie CA, Stilp AM, et al. Genetic diversity and association studies in US Hispanic/Latino. Populations: Applications in the Hispanic Community Health Study/Study of Latinos. Am J Hum Genet; 2016.  https://doi.org/10.1016/j.ajhg.2015.12.001.CrossRefGoogle Scholar
  19. 19.
    Bird A. DNA methylation patterns and epigenetic memory. Genes Dev. 2002;16:6–21.  https://doi.org/10.1101/gad.947102.CrossRefPubMedGoogle Scholar
  20. 20.
    Muka T, Nano J, Voortman T, et al. The role of global and regional DNA methylation and histone modifications in glycemic traits and type 2 diabetes: a systematic review. Nutr Metab Cardiovasc Dis. 2016;26:553–66.  https://doi.org/10.1016/j.numecd.2016.04.002.CrossRefPubMedPubMedCentralGoogle Scholar
  21. 21.
    Van Dijk SJ, Molloy PL, Varinli H, et al. Epigenetics and human obesity. Int J Obes. 2015.Google Scholar
  22. 22.
    Mamtani M, Kulkarni H, Dyer TD, et al. Genome- and epigenome-wide association study of hypertriglyceridemic waist in Mexican American families. Clin Epigenetics. 2016.  https://doi.org/10.1186/s13148-016-0173-x.
  23. 23.
    Kulkarni H, Kos MZ, Neary J, et al. Novel epigenetic determinants of type 2 diabetes in Mexican-American families. Hum Mol Genet. 2015.  https://doi.org/10.1093/hmg/ddv232.
  24. 24.
    Carless MA, Kulkarni H, Kos MZ, et al. Genetic effects on DNA methylation and its potential relevance for obesity in Mexican Americans. PLoS One. 2013.  https://doi.org/10.1371/journal.pone.0073950.
  25. 25.
    Alegría-Torres JA, Baccarelli A, Bollati V. Epigenetics and lifestyle. Epigenomics. 2011;3:267–77.  https://doi.org/10.2217/epi.11.22.CrossRefPubMedPubMedCentralGoogle Scholar
  26. 26.
    • Santos HP, Nephew BC, Bhattacharya A, et al. Discrimination exposure and DNA methylation of stress-related genes in Latina mothers. Psychoneuroendocrinology. 2018;98:131–8.  https://doi.org/10.1016/j.psyneuen.2018.08.014. Study considers percieved descrimination and its association with DNA methylation over time in a Hispanic/Latino cohort. CrossRefPubMedPubMedCentralGoogle Scholar
  27. 27.
    •• Smith JA, Zhao W, Wang X, et al. Neighborhood characteristics influence DNA methylation of genes involved in stress response and inflammation: The Multi-Ethnic Study of Atherosclerosis. Epigenetics. 2017.  https://doi.org/10.1080/15592294.2017.1341026. An excellent example of a study that integrates neighborhood, epigenomics, and biomarker outcomes to understand health disparities. The study considers several components of neighborhood context and finds several influence DNA methylatoin on stress and inflammation-related genes after accounting for individual covariates.
  28. 28.
    Olden K, Lin YS, Gruber D, Sonawane B. Epigenome: biosensor of cumulative exposure to chemical and nonchemical stressors related to environmental justice. Am. J: Public Health; 2014.Google Scholar
  29. 29.
    Giurgescu C, Nowak AL. Gillespie S, et al. Neighborhood Environment and DNA Methylation: Implications for Cardiovascular Disease Risk. J. Urban Heal; 2019.Google Scholar
  30. 30.
    Cho I, Blaser MJ. The human microbiome: at the interface of health and disease. Nat Rev Genet. 2012.  https://doi.org/10.1038/nrg3182.
  31. 31.
    Fortenberry JD. The uses of race and ethnicity in human microbiome research. Trends Microbiol. 2013;21:165–6.CrossRefGoogle Scholar
  32. 32.
    Castaner O, Goday A, Park YM, et al. The gut microbiome profile in obesity: a systematic review. Int J Endocrinol. 2018.  https://doi.org/10.1155/2018/4095789.
  33. 33.
    •• Mitchell CM, Davy BM, Hulver MW, et al. Does exercise Alter gut microbial composition? A systematic review. Med Sci Sports Exerc. 2019. A first review of interplay between gut microbiome and physical activity finding that results are currently mixed partially due to lack of consistency in physical activity measurement methods. Google Scholar
  34. 34.
    David LA, Maurice CF, Carmody RN, et al. Diet rapidly and reproducibly alters the human gut microbiome. Nature. 2013;505:559–63.  https://doi.org/10.1038/nature12820.CrossRefPubMedPubMedCentralGoogle Scholar
  35. 35.
    Voreades N, Kozil A, Weir TL. Diet and the development of the human intestinal microbiome. Front Microbiol. 2014;5.  https://doi.org/10.3389/fmicb.2014.00494.
  36. 36.
    Xu Z, Knight R. Dietary effects on human gut microbiome diversity. Br J Nutr. 2014;113(Suppl):1–5.  https://doi.org/10.1017/S0007114514004127.CrossRefGoogle Scholar
  37. 37.
    Miller GE, Engen PA, Gillevet PM, et al. Lower neighborhood socioeconomic status associated with reduced diversity of the colonic microbiota in healthy adults. PLoS One. 2016.  https://doi.org/10.1371/journal.pone.0148952.
  38. 38.
    • Alderete TL, Jones RB, Chen Z, et al. Exposure to traffic-related air pollution and the composition of the gut microbiota in overweight and obese adolescents. Environ Res. 2018.  https://doi.org/10.1016/j.envres.2017.11.046. First paper to show how air pollution may be influencing obesity in adolescents through a gut microbiome mechanism.
  39. 39.
    • Hoffman KL, Hutchinson DS, Fowler J, et al. Oral microbiota reveals signs of acculturation in Mexican American women. PLoS One. 2018.  https://doi.org/10.1371/journal.pone.0194100. Novel approach for understanding how acculturation may be influencing health by assessing oral microbial diversity.
  40. 40.
    Chen M-W, Ye S, Zhao L-L, et al. Association of plasma total and high-molecular-weight adiponectin with risk of colorectal cancer: an observational study in Chinese male. Med Oncol. 2012;29:1–7.  https://doi.org/10.1007/s12032-012-0280-2.CrossRefGoogle Scholar
  41. 41.
    Dash S. Causes of severe obesity: genes to environment. In: Sockalingam S, Hawa R, editors. Psychiatric Care in Severe Obesity. Cham: Springer; 2017. p. 21–36.CrossRefGoogle Scholar
  42. 42.
    Martinez JA, Milagro FI, Claycombe KJ, Schalinske KL. Epigenetics in adipose tissue, obesity, weight loss, and diabetes. Adv Nutr An Int Rev J. 2014;5:71–81.  https://doi.org/10.3945/an.113.004705.CrossRefGoogle Scholar
  43. 43.
    •• Dang J, Yang M, Zhang X, et al (2018) Associations of Exposure to Air Pollution with Insulin Resistance: A Systematic Review and Meta-Analysis. Int J Environ Res Public Health 15:.  https://doi.org/10.3390/ijerph15112593. Excellent review of current research linking air pollution to insulin resistance.
  44. 44.
    Petrovic D, de Mestral C, Bochud M, et al. The contribution of health behaviors to socioeconomic inequalities in health: a systematic review. Prev Med (Baltim). 2018;113:15–31.  https://doi.org/10.1016/j.ypmed.2018.05.003.CrossRefGoogle Scholar
  45. 45.
    Slopen N, Lewis TT, Williams DR. Discrimination and sleep: a systematic review. Sleep Med. 2016.Google Scholar
  46. 46.
    •• Jackson CL (2017) Determinants of racial/ethnic disparities in disordered sleep and obesity. Sleep heal.  https://doi.org/10.1016/j.sleh.2017.08.001. Thoughtful review and framework for undersatnding how racial and ethnic disparities in sleep are influencing obesity, associated mechanisms, and enironmental causes.
  47. 47.
    Chen X, Wang R, Zee P, et al. Racial/ethnic differences in sleep disturbances: the multi-ethnic study of atherosclerosis (MESA). Sleep. 2015;38:877–88.  https://doi.org/10.5665/sleep.4732.CrossRefPubMedPubMedCentralGoogle Scholar
  48. 48.
    • Knutson KL, Wu D, Patel SR, et al. Association between sleep timing, obesity, diabetes: the hispanic community health study/study of latinos (hchs/sol) cohort study. Sleep. 2017.  https://doi.org/10.1093/sleep/zsx014. One of the first larger studies to utilize accelerometer measured sleep and relate both sleep disturbances and length of sleep to obesity and diabetes in Hispanic/Latinos.
  49. 49.
    Ramos AR, Weng J, Wallace DM, et al. Sleep patterns and hypertension using Actigraphy in the Hispanic community health study/study of Latinos. Chest. 2018.  https://doi.org/10.1016/j.chest.2017.09.028.
  50. 50.
    Loredo SJ, Weng HJ, Ramos AR, et al. Sleep patterns and obesity: Hispanic community health study/study of Latinos Sueño Ancillar study. Chest. 2019;156:348–56.  https://doi.org/10.1016/j.chest.2018.12.004.CrossRefPubMedPubMedCentralGoogle Scholar
  51. 51.
    Billings ME, Gold DR, Leary PJ, et al. Relationship of air pollution to sleep disruption: the multi-ethnic study of atherosclerosis (MESA) sleep and MESA-air studies. Am J Respir Crit Care Med. 2017;195:A2930.Google Scholar
  52. 52.
    • Simonelli G, Dudley KA, Weng J, et al. Neighborhood Factors as Predictors of Poor Sleep in the Sueño Ancillary Study of the Hispanic Community Health Study/Study of Latinos. Sleep. 2017;40.  https://doi.org/10.1093/sleep/zsw025. This study extends literature showing negative health effects of adverse neighborhood factors and finds that percieved safety, violence and noise had impacts on length and quality of sleep in a cohort of Hispanic/Latinos.
  53. 53.
    Leal C, Chaix B. The influence of geographic life environments on cardiometabolic risk factors: a systematic review, a methodological assessment and a research agenda. Obes Rev. 2011;12:217–30.  https://doi.org/10.1111/j.1467-789X.2010.00726.x.CrossRefPubMedPubMedCentralGoogle Scholar
  54. 54.
    Sallis JF, Floyd MF, Rodriguez DA, Saelens BE. The role of built environments in physical activity, obesity, and CVD. Circulation. 2012;125:729–37.  https://doi.org/10.1161/CIRCULATIONAHA.110.969022.CrossRefPubMedPubMedCentralGoogle Scholar
  55. 55.
    Feng J, Glass TA, Curriero FC, et al. The built environment and obesity: a systematic review of the epidemiologic evidence. Health Place. 2010;16:175–90.  https://doi.org/10.1016/j.healthplace.2009.09.008.CrossRefPubMedPubMedCentralGoogle Scholar
  56. 56.
    Lovasi GS, Hutson MA, Guerra M, Neckerman KM. Built environments and obesity in disadvantaged populations. Epidemiol Rev. 2009;31:7–20.  https://doi.org/10.1093/epirev/mxp005.CrossRefPubMedGoogle Scholar
  57. 57.
    Piccolo RS, Duncan DT, Pearce N, McKinlay JB. The role of neighborhood characteristics in racial/ethnic disparities in type 2 diabetes: results from the Boston area community health (BACH) survey. Soc Sci Med. 2015;130:79–90.  https://doi.org/10.1016/j.socscimed.2015.01.041.CrossRefPubMedPubMedCentralGoogle Scholar
  58. 58.
    Wen M, Maloney TN. Latino residential isolation and the risk of obesity in Utah: the role of neighborhood socioeconomic, built-environmental, and subcultural context. J Immigr Minor Health. 2011;13:1134–41.  https://doi.org/10.1007/s10903-011-9439-8.CrossRefPubMedPubMedCentralGoogle Scholar
  59. 59.
    Fields R, Kaczynski A, Bopp M, Fallon E. Built environment associations with health behaviors among Hispanics. J Phys Act Health. 2013;10:355–42.CrossRefGoogle Scholar
  60. 60.
    Paradies Y, Ben J, Denson N, et al. Racism as a determinant of health: a systematic review and meta-analysis. PLoS One. 2015.  https://doi.org/10.1371/journal.pone.0138511.
  61. 61.
    • Bell CN, Kerr J, Young JL. Associations between obesity, obesogenic environments, and structural racism vary by county-level racial composition. Int J Environ Res Public Health. 2019.  https://doi.org/10.3390/ijerph16050861. One of the first studies to implement a county level measure of racial inequality by SES level across the United States to find that inequality was associated with obesity and obesogenic environments.
  62. 62.
    • Bailey ZD, Krieger N, Agénor M, et al. Structural racism and health inequities in the USA: evidence and interventions. Lancet. 2017. An important piece that lays out various ways that structural racism impacts health inequalities, but also ways to assess and measure strucutral racism in epidemiological studies and interventions. Google Scholar
  63. 63.
    Castle B, Wendel M, Kerr J, et al. Public Health’s approach to systemic racism: a systematic literature review. Disparities: J. Racial Ethn. Heal; 2019.Google Scholar
  64. 64.
    Müller R, Hanson C, Hanson M, et al. The biosocial genome? EMBO Rep. 2017;18.  https://doi.org/10.15252/embr.201744953.
  65. 65.
    Darling KW, Ackerman SL, Hiatt RH, et al. Enacting the molecular imperative: how gene-environment interaction research links bodies and environments in the post-genomic age. Soc Sci Med. 2016;155:51–60.  https://doi.org/10.1016/j.socscimed.2016.03.007.CrossRefPubMedPubMedCentralGoogle Scholar
  66. 66.
    Senier L, Brown P, Shostak S, Hanna B. The socio-exposome: advancing exposure science and environmental justice in a postgenomic era. Environ Sociol. 2017;3.  https://doi.org/10.1080/23251042.2016.1220848.
  67. 67.
    Liu C, Maity A, Lin X, et al. Design and analysis issues in gene and environment studies. Environ Health. 2012;11:93.  https://doi.org/10.1186/1476-069X-11-93.CrossRefPubMedPubMedCentralGoogle Scholar
  68. 68.
    Kerr J, Patterson RE, Ellis K, et al. Objective assessment of physical activity: classifiers for public health. Med Sci Sports Exerc. 2016.  https://doi.org/10.1249/MSS.0000000000000841.
  69. 69.
    Troiano RP, Berrigan D, Dodd KW, et al. Physical activity in the United States measured by accelerometer. Med Sci Sports Exerc. 2008;40:181–8.  https://doi.org/10.1249/mss.0b013e31815a51b3.CrossRefPubMedPubMedCentralGoogle Scholar
  70. 70.
    Ellis K, Kerr J, Godbole S, et al. Hip and wrist accelerometer algorithms for free-living behavior classification objective measurement of physical activity. Med Sci Sports Exerc. 2016;48:933–40.  https://doi.org/10.1249/MSS.0000000000000840.CrossRefPubMedPubMedCentralGoogle Scholar
  71. 71.
    Dodge HH, Zhu J, Mattek NC, et al. Use of high-frequency in-home monitoring data may reduce sample sizes needed in clinical trials. PLoS One. 2015:10.  https://doi.org/10.1371/journal.pone.0138095.
  72. 72.
    Krenn PJ, Titze S, Oja P, et al. Use of global positioning systems to study physical activity and the environment: a systematic review. Am J Prev Med. 2011;41:508–15.  https://doi.org/10.1016/j.amepre.2011.06.046.CrossRefPubMedPubMedCentralGoogle Scholar
  73. 73.
    Jankowska MM, Schipperijn J, Kerr J. A framework for using GPS data in physical activity and sedentary behavior studies. Exerc Sport Sci Rev. 2015;43:48–56.CrossRefGoogle Scholar
  74. 74.
    Berrigan D, Hipp A, Hurvitz PM, et al. Geospatial and contextual approaches to energy balance and health. Ann GIS. 2015;21:157–68.  https://doi.org/10.1080/19475683.2015.1019925.CrossRefPubMedPubMedCentralGoogle Scholar
  75. 75.
    Rainham D, McDowell I, Krewski D, Sawada M. Conceptualizing the healthscape: contributions of time geography, location technologies and spatial ecology to place and health research. Soc Sci Med. 2010;70:668–76.  https://doi.org/10.1016/j.socscimed.2009.10.035.CrossRefPubMedPubMedCentralGoogle Scholar
  76. 76.
    Kim D, Joung JG, Sohn KA, et al. Knowledge boosting: a graph-based integration approach with multi-omics data and genomic knowledge for cancer clinical outcome prediction. J Am Med Inform Assoc. 2015.  https://doi.org/10.1136/amiajnl-2013-002481.
  77. 77.
    • Huang S, Chaudhary K, Garmire LX. More is better: recent progress in multi-omics data integration methods. Front Genet. 2017. A good review of varoius methods for heterogenous data integration methods in the omics sciences. Google Scholar
  78. 78.
    Pastrello C, Pasini E, Kotlyar M, et al. Integration, visualization and analysis of human interactome. Biochem Biophys Res Commun. 2014.Google Scholar
  79. 79.
    Peng C, Wang J, Asante I, et al. A latent unknown clustering integrating multi-Omics data (LUCID) with phenotypic traits. Bioinformatics. 2019.  https://doi.org/10.1093/bioinformatics/btz667.
  80. 80.
    Jankowska MM, Sears DD, Natarajan L, et al. Protocol for a cross sectional study of cancer risk, environmental exposures and lifestyle behaviors in a diverse community sample: the Community of Mine study. BMC Public Health. 2019;19.  https://doi.org/10.1186/s12889-019-6501-2.
  81. 81.
    McDonald D, Hyde E, Debelius JW, et al American Gut: an Open Platform for Citizen-Science Microbiome Research. Science (80- ).Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Marta M. Jankowska
    • 1
    Email author
  • Kyle Gaulton
    • 2
  • Rob Knight
    • 2
    • 3
    • 4
    • 5
  • Kevin Patrick
    • 1
    • 6
  • Dorothy D. Sears
    • 3
    • 5
    • 6
    • 7
    • 8
  1. 1.Qualcomm Institute/Calit2, 9500 Gilman Drive MC 0811University of California San DiegoSan DiegoUSA
  2. 2.Department of PediatricsUC San DiegoSan DiegoUSA
  3. 3.Center for Microbiome InnovationUC San DiegoSan DiegoUSA
  4. 4.Department of Computer Science and EngineeringUC San DiegoSan DiegoUSA
  5. 5.Center for Circadian BiologyUC San DiegoSan DiegoUSA
  6. 6.Department of Family Medicine and Public HealthUC San DiegoSan DiegoUSA
  7. 7.College of Health SolutionsArizona State UniversityPhoenixUSA
  8. 8.Department of MedicineUC San DiegoSan DiegoUSA

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